Monitoring medical assistance in dying (MAiD) in Canada: Perspectives of physicians, nurse practitioners, and organizational regulatory actors
Bibliographic record
Abstract
Canada's federal monitoring system for medical assistance in dying (MAiD) commenced in 2018 and was expanded in 2023 to enhance data collection. This article sought to understand the role of monitoring in the regulation of MAiD in Canada. It reports on qualitative interviews conducted with 68 participants from two key groups: MAiD assessors and providers; and "organizational actors" from a range of bodies including government, regulators, professional organizations, and healthcare organizations. Participants' views of the monitoring framework for MAiD were analyzed. There was consensus that monitoring should be distinguished from oversight. Participants thought the monitoring system provided important transparency into MAiD practice but emphasized mitigating burdens on practitioners, where possible. Methods of data collection varied, and a pan-Canadian approach was challenging. Participants had different views about the appropriate scope of data. The article concludes with recommendations for effective monitoring of assisted dying practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.020 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".